{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f355316a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from gernerate_data import load_clas_seg_data\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import classification_report, auc, roc_curve\n",
    "from keras.utils.np_utils import *\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from models.MTL_IBA import MTL_IBA, MTL_IBA_h3, MTL_IBA_cross3,MTL_IBA_s2c, MTL_IBA_cross3_pretrain\n",
    "from models.MTL_Attention import MTL_Attention_model\n",
    "# from models.shareLayer import create_pair_model\n",
    "from sklearn.preprocessing import LabelBinarizer, label_binarize\n",
    "from utils.losses import dice_coef_loss, dice_coef, dice_p_bce, dice_p_focal, tversky_loss, focal_loss, focal_tversky, \\\n",
    "    p_r_f1_iou, generalized_dice_coeff, generalized_dice_loss\n",
    "from keras.losses import categorical_crossentropy, mean_squared_error, binary_crossentropy\n",
    "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping, CSVLogger\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "import keras.backend as K\n",
    "import utils_paths\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ed814ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------ Preparing model ------------------------------------------------\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "lamb################################# Tensor(\"iba/Sigmoid:0\", shape=(1, 28, 28, 256), dtype=float32)\n",
      "Z_with_passing################################# Tensor(\"iba/mul_6:0\", shape=(?, 28, 28, 256), dtype=float32)\n",
      "output################################# Tensor(\"iba/cond_2/Merge:0\", shape=(?, 28, 28, 256), dtype=float32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-12 13:16:39.551645: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "2022-04-12 13:16:39.581265: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200095000 Hz\n",
      "2022-04-12 13:16:39.585678: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x63d7950 executing computations on platform Host. Devices:\n",
      "2022-04-12 13:16:39.585797: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2022-04-12 13:16:40.205641: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x6483980 executing computations on platform CUDA. Devices:\n",
      "2022-04-12 13:16:40.205703: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla V100-PCIE-32GB, Compute Capability 7.0\n",
      "2022-04-12 13:16:40.206777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
      "name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38\n",
      "pciBusID: 0000:05:00.0\n",
      "totalMemory: 31.72GiB freeMemory: 30.66GiB\n",
      "2022-04-12 13:16:40.206812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2022-04-12 13:16:40.209273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2022-04-12 13:16:40.209309: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2022-04-12 13:16:40.209323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2022-04-12 13:16:40.210146: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 29826 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:05:00.0, compute capability: 7.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "INIT_LR = 2e-4\n",
    "EPOCHS =200\n",
    "batch_size = 8\n",
    "batch_size2 = 8\n",
    "depth = 3\n",
    "img_size = 224\n",
    "Name = \"MTL_IBA_cross3_pretrain\"\n",
    "GPU = True\n",
    "target = (img_size, img_size)\n",
    "\n",
    "if GPU:\n",
    "    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "\n",
    "    \n",
    "print(\"------------------------------------------------ Preparing model ------------------------------------------------\")\n",
    "model = MTL_IBA_cross3(img_size, img_size, depth, nClasses=2)\n",
    "# model = MTL_Attention_model(img_size, img_size, depth, nClasses=2)\n",
    "model.summary()\n",
    "# model.load_weights(\" MTL_IBA_cross3_pretrain2.h5\", by_name=True)\n",
    "\n",
    "# model.load_weights(\"MTL_IBA_cross3_pretrain-043-0.9894.h5\", by_name=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05219e5d",
   "metadata": {},
   "source": [
    "## 如果需要预训练分类分支，执行以下代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b92492f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------data loading-----\n",
      "dataset/Dataset_BUSI_AN4finetune/train/images/bengin_images/benign (114).png\n",
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      "dataset/Dataset_BUSI_AN4finetune/train/images/bengin_images/benign (210).png\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (201).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (123).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (408).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (44).png\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import utils_paths\n",
    "import random\n",
    "import cv2\n",
    "\n",
    "def scheduler(epoch):\n",
    "\n",
    "    if epoch % 100 == 0 and epoch != 0:\n",
    "        lr = K.get_value(model.optimizer.lr)\n",
    "        K.set_value(model.optimizer.lr, lr * 0.1)\n",
    "        print(\"lr changed to {}\".format(lr * 0.1))\n",
    "    return K.get_value(model.optimizer.lr)\n",
    "\n",
    "def load_cla_data(train_imagePaths, target):\n",
    "\n",
    "    data = []\n",
    "    labels = []\n",
    "\n",
    "    for imagePath in train_imagePaths:\n",
    "\n",
    "        image = cv2.imread(imagePath)\n",
    "        image = cv2.resize(image, target)\n",
    "        data.append(image)\n",
    "        print(imagePath)\n",
    "        label = imagePath.split(os.path.sep)[-2]\n",
    "        labels.append(label)\n",
    "\n",
    "    data = np.array(data, dtype=\"float\")\n",
    "    #data = data.swapaxes(1, 3)\n",
    "    data = data / 255.0\n",
    "    labels = np.array(labels)\n",
    "\n",
    "    return data, labels\n",
    "\n",
    "print(\"------data loading-----\")\n",
    "train_path = 'dataset/Dataset_BUSI_AN4finetune/train/images/'# train_path = './dataset/OCT/train/'\n",
    "test_path = train_path.replace('train', 'test')\n",
    "val_path = train_path.replace('train', 'val')\n",
    "\n",
    "train_imagePaths = sorted(list(utils_paths.list_images(train_path)))\n",
    "val_imagePaths = sorted(list(utils_paths.list_images(val_path)))\n",
    "test_imagePaths = sorted(list(utils_paths.list_images(test_path)))\n",
    "random.seed(307)\n",
    "random.shuffle(train_imagePaths)\n",
    "random.shuffle(val_imagePaths)\n",
    "random.shuffle(test_imagePaths)\n",
    "\n",
    "\n",
    "train_x, train_y = load_cla_data(train_imagePaths, target)\n",
    "val_x, val_y = load_cla_data(val_imagePaths, target)\n",
    "test_x, test_y = load_cla_data(test_imagePaths, target)\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "train_y = lb.fit_transform(train_y)\n",
    "val_y = lb.fit_transform(val_y)\n",
    "test_y = lb.fit_transform(test_y)\n",
    "# train_y = label_binarize(train_y, np.arange(2))\n",
    "train_y = to_categorical(train_y, 2)\n",
    "val_y = to_categorical(val_y, 2)\n",
    "test_y = to_categorical(test_y, 2)\n",
    "\n",
    "# # 数据增强处理\n",
    "# aug = ImageDataGenerator(rotation_range=30,\n",
    "#                          # brightness_range=(0.5, 0.9),\n",
    "#                          # rescale=0.8,\n",
    "#                          shear_range=0.2,\n",
    "#                          zoom_range=0.1,\n",
    "#                          horizontal_flip=True,\n",
    "#                          # vertical_flip=True,\n",
    "#                          fill_mode=\"nearest\"\n",
    "#                          )\n",
    "\n",
    "print(\"------begin training -----\")\n",
    "opt = Adam(lr=1e-4, beta_1=0.9, beta_2=0.99, epsilon=1e-08, decay=0.01)\n",
    "#opt = SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
    "# opt = Adam(lr=INIT_LR)\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n",
    "# model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n",
    "\n",
    "\n",
    "# define callbacks\n",
    "csv_logger = CSVLogger(Name+'.log')\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=20, min_lr=1e-8, mode='auto', verbose=1)\n",
    "\n",
    "checkpoint_period1 = ModelCheckpoint(Name + '-{epoch:03d}-{val_acc:.4f}.h5',\n",
    "                                     monitor='val_acc', mode='auto', save_best_only='True', period=20)\n",
    "\n",
    "checkpoint_period2 = ModelCheckpoint(Name + '-{epoch:03d}-{val_acc:.4f}.h5',\n",
    "                                     monitor='val_acc', mode='auto', period=20)\n",
    "\n",
    "# earlyStopping = EarlyStopping(monitor='val_acc', patience=20, mode='auto')\n",
    "reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=10, min_lr=1e-8, mode='auto', verbose=1)\n",
    "#\n",
    "#\n",
    "hist = model.fit(train_x, train_y,\n",
    "                 batch_size=16,\n",
    "                 epochs=EPOCHS,\n",
    "                 validation_data=(val_x, val_y),\n",
    "                 class_weight=\"auto\",\n",
    "                 verbose=1,\n",
    "                 callbacks=[reduce_lr, checkpoint_period1, csv_logger])\n",
    "\n",
    "# hist = model.fit_generator(aug.flow(train_x, train_y, batch_size=BS),\n",
    "#                            validation_data=aug.flow(val_x, val_y),\n",
    "#                            steps_per_epoch=len(train_x) // BS,\n",
    "#                            class_weight={0: 0.8, 1: 1.2},\n",
    "#                            validation_steps=5,\n",
    "#                            workers=6,\n",
    "#                            epochs=EPOCHS, callbacks=[checkpoint_period1, checkpoint_period2, csv_logger, reduce_lr])\n",
    "\n",
    "print(\"------Saving model------\")\n",
    "# model.save('vgg16_150_ICIS_1-700X700.h5')\n",
    "model_filename = Name + \".h5\"\n",
    "model.save_weights(model_filename)\n",
    "print('model saved to:', model_filename)\n",
    "\n",
    "\n",
    "print(\"------ testing ------\")\n",
    "predictions = model.predict(test_x, batch_size=32)\n",
    "print(classification_report(test_y.argmax(axis=1),\n",
    "      predictions.argmax(axis=1), digits=6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279bfbf3",
   "metadata": {},
   "source": [
    "## 训练多任务模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7479810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/Dataset_BUSI_AN4finetune/val/images/bengin_images/benign (356).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/bengin_masks/benign (356).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/bengin_images/benign (90)_15dbd368-3674-418d-b013-240f0949ee18.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/bengin_masks/benign (90)_15dbd368-3674-418d-b013-240f0949ee18.png\n",
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      "dataset/Dataset_BUSI_AN4finetune/val/masks/bengin_masks/benign (162)_a219440b-12d0-4b84-ab54-749ca8c6b2d4.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/bengin_images/benign (336).png\n",
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      "dataset/Dataset_BUSI_AN4finetune/val/masks/bengin_masks/benign (198)_23f25af2-2abc-4d85-a04e-7c7f0e2b1b5e.png\n",
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      "dataset/Dataset_BUSI_AN4finetune/val/masks/bengin_masks/benign (361)_4d5a28f6-9b60-43a8-9d0a-1337ecac0c9b.png\n",
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      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (6)_d18758a4-1542-4c75-a5de-4ffd80a06641.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (195)_4d5b2a8a-2926-48b9-bbf5-d5fddfa8aceb.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (195)_4d5b2a8a-2926-48b9-bbf5-d5fddfa8aceb.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (184)_aabd0ba0-44d2-4f64-8ce9-2bf0449fb43f.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (184)_aabd0ba0-44d2-4f64-8ce9-2bf0449fb43f.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (180).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (180).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (36)_bed6a151-854d-44c5-bbba-9723c2f3361a.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (36)_bed6a151-854d-44c5-bbba-9723c2f3361a.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (51).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (51).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (103).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (103).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (61).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (61).png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/images/malignant_images/malignant (197)_de5527df-1db4-491a-a221-e1d17e1d251d.png\n",
      "dataset/Dataset_BUSI_AN4finetune/val/masks/malignant_masks/malignant (197)_de5527df-1db4-491a-a221-e1d17e1d251d.png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (220).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (220).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (316).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (316).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (231).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (231).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (153).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (153).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (21).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (21).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (147).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (147).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (324).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (324).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (391).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (391).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (222).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (222).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (81).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (81).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (221).png\n",
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      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (38).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (38).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (127).png\n",
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      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (136).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (425).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (425).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (88).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (88).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (57).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (57).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (167).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (167).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (187).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (187).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (321).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (321).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (15).png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (15).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (104).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (104).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (388).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (388).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (353).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (353).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (48).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (48).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/bengin_images/benign (299).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/bengin_masks/benign (299).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (74).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (74).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (200).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (200).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (136).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (136).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (121).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (121).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (131).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (131).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (149).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (149).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (191).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (191).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (112).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (112).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (190).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (190).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (72).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (72).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (18).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (18).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (146).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (146).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (20).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (20).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (65).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (65).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (106).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (106).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (170).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (170).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (123).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (123).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (44).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (44).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (171).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (171).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (45).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (45).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/images/malignant_images/malignant (86).png\n",
      "dataset/Dataset_BUSI_AN4finetune/test/masks/malignant_masks/malignant (86).png\n"
     ]
    }
   ],
   "source": [
    "# trainX_dir = 'dataset/LE/test/images/'\n",
    "# trainX_dir = 'dataset/LE-NEW/train/images'\n",
    "# trainX_dir = 'dataset/Dataset_BUSI_AN/train/images'\n",
    "trainX_dir = 'dataset/Dataset_BUSI_AN4finetune/val/images'\n",
    "testX_dir = trainX_dir.replace('val', 'test')\n",
    "\n",
    "\n",
    "train_x,  train_c_y, train_s_y = load_clas_seg_data(trainX_dir, target)\n",
    "# val_x, val_c_y, val_s_y = load_clas_seg_data(valX_dir, target)\n",
    "test_x, test_c_y, test_s_y = load_clas_seg_data(testX_dir, target)\n",
    "\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "train_c_y = lb.fit_transform(train_c_y)\n",
    "# val_c_y = lb.fit_transform(val_c_y)\n",
    "test_c_y = lb.fit_transform(test_c_y)\n",
    "train_c_y = to_categorical(train_c_y, 2)\n",
    "# val_c_y = to_categorical(val_c_y, 2)\n",
    "test_c_y = to_categorical(test_c_y, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6931d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from models.shareLayer import create_pair_model\n",
    "# from models.MLT_net import MTL_classic, MTL_3\n",
    "\n",
    "# model = MTL_classic(img_size, img_size, depth, nClasses=2)\n",
    "# model = MTL_3(img_size, img_size, depth, nClasses=2)\n",
    "# model = create_pair_model(img_size, img_size, depth, nClasses=2)\n",
    "# model.summary()\n",
    "\n",
    "\n",
    "# define callbacks\n",
    "csv_logger = CSVLogger(Name+'.log')\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='segmentation_output_loss', factor=0.2, patience=10, min_lr=1e-8, mode='auto', verbose=1)\n",
    "\n",
    "checkpoint_period1 = ModelCheckpoint(Name + '-{epoch:03d}-{segmentation_output_acc:.4f}.h5',\n",
    "                                     monitor='segmentation_output_acc', mode='auto', save_best_only='True')\n",
    "\n",
    "# define loss and compile the model\n",
    "\n",
    "opt = Adam(lr=INIT_LR, beta_1=0.9, beta_2=0.99, epsilon=1e-08, decay=0.01)\n",
    "\n",
    "def binary_crossentropy(y_true, y_pred):\n",
    "    e=1.0\n",
    "    return K.mean(-(y_true*K.log(y_pred+K.epsilon())+\n",
    "                    e*(1-y_true)*K.log(1-y_pred+K.epsilon())),\n",
    "                  axis=-1)\n",
    "\n",
    "def focal_loss(y_true, y_pred, alpha=0.25, gamma=2):\n",
    "    epsilon = 1e-5\n",
    "    y_pred = tf.clip_by_value(y_pred, epsilon, 1 - epsilon)\n",
    "    logits = tf.log(y_pred / (1 - y_pred))\n",
    "    weight_a = alpha * tf.pow((1 - y_pred), gamma) * y_true\n",
    "    weight_b = (1 - alpha) * tf.pow(y_pred, gamma) * (1 - y_true)\n",
    "    loss = tf.log1p(tf.exp(-logits)) * (weight_a + weight_b) + logits * weight_b\n",
    "    return tf.reduce_mean(loss)\n",
    "\n",
    "def tversky(y_true, y_pred, smooth=1):\n",
    "    y_true_pos = K.flatten(y_true)\n",
    "    y_pred_pos = K.flatten(y_pred)\n",
    "    true_pos = K.sum(y_true_pos * y_pred_pos)\n",
    "    false_neg = K.sum(y_true_pos * (1 - y_pred_pos))\n",
    "    false_pos = K.sum((1 - y_true_pos) * y_pred_pos)\n",
    "    alpha = 0.5\n",
    "    return (true_pos + smooth) / (true_pos + alpha * false_neg + (1 - alpha) * false_pos + smooth)\n",
    "\n",
    "def tversky_loss(y_true, y_pred):\n",
    "    return 1 - tversky(y_true, y_pred)\n",
    "\n",
    "\n",
    "def focal_tversky(y_true,y_pred):\n",
    "    pt_1 = tversky(y_true, y_pred)\n",
    "    gamma = 0.75\n",
    "    return K.pow((1-pt_1), gamma)\n",
    "\n",
    "from keras.losses import categorical_crossentropy\n",
    "\n",
    "# model.compile(loss={'segmentation_output': categorical_crossentropy, 'seg-out': tversky_loss, \"classification_output\": binary_crossentropy},\n",
    "#               loss_weights={'segmentation_output': 0.25,'seg-out': 0.5, \"classification_output\": 0.5},\n",
    "#               optimizer=opt, \n",
    "#               metrics={'segmentation_output':'accuracy', 'seg-out':dice_coef, 'classification_output':'accuracy'})\n",
    "\n",
    "model.compile(loss={'segmentation_output': categorical_crossentropy, 'seg-out': tversky_loss, \"classification_output\": binary_crossentropy},\n",
    "              loss_weights={'segmentation_output': 0.35,'seg-out': 0.35, \"classification_output\": 0.3},\n",
    "              optimizer=opt, \n",
    "              metrics={'segmentation_output':'accuracy', 'seg-out':dice_coef, 'classification_output':'accuracy'})\n",
    "\n",
    "\n",
    "# # model.compile(loss={'segmentation_output': focal_tversky, \"classification_output\": binary_crossentropy},\n",
    "# model.compile(loss={'segmentation_output': 'categorical_crossentropy', \"classification_output\": binary_crossentropy},\n",
    "#               loss_weights={'segmentation_output': 0.5, \"classification_output\": 0.5},\n",
    "#               optimizer=Adam(lr=1e-3), \n",
    "#               metrics={'segmentation_output':['accuracy', dice_coef], 'classification_output':'accuracy'})\n",
    "\n",
    "# model.compile(loss={'seg_out': 'categorical_crossentropy', 'cls_out': binary_crossentropy},\n",
    "#               optimizer=Adam(lr=1e-4), metrics={'seg_out':dice_coef, 'cls_out':'accuracy'},\n",
    "#               loss_weights={'seg_out':0.5, 'cls_out':0.5})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2051d8",
   "metadata": {},
   "source": [
    "## 训练前加载权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c86ae4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"MTL_IBA_cross3_pretrain_clas.h5\", by_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed67d51e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------Start predictiion------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-12 05:22:42.343619: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------ Classification testing ------------------------------------------------\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "   bengin_images   0.895833  1.000000  0.945055        43\n",
      "malignant_images   1.000000  0.761905  0.864865        21\n",
      "\n",
      "        accuracy                       0.921875        64\n",
      "       macro avg   0.947917  0.880952  0.904960        64\n",
      "    weighted avg   0.930013  0.921875  0.918743        64\n",
      "\n",
      "------------------------------------------------ Segmentation testing ------------------------------------------------\n",
      "Test total loss: 0.20122163742780685\n",
      "Test classification loss: 0.43203072249889374\n",
      "Test segmentation loss: 0.1599339246749878\n",
      "Test classification accuracy: 0.921875\n",
      "Test segmentation accuracy: 0.9560612142086029\n",
      "Test segmentation dice_coef: 0.9553269147872925\n",
      "64/64 [==============================] - 3s 40ms/step\n",
      "AUC: 0.9870764074620785\n"
     ]
    }
   ],
   "source": [
    "print(\"------Start predictiion------\")\n",
    "\n",
    "\n",
    "predictions_c, predictions_s, predictions_s2 = model.predict(test_x, batch_size=32)\n",
    "\n",
    "\n",
    "print(\"------------------------------------------------ Classification testing ------------------------------------------------\")\n",
    "\n",
    "print(classification_report(test_c_y.argmax(axis=1), predictions_c.argmax(axis=1), target_names=lb.classes_, digits=6))\n",
    "\n",
    "print(\"------------------------------------------------ Segmentation testing ------------------------------------------------\")\n",
    "\n",
    "\n",
    "# evaluate the model\n",
    "# loss = model.evaluate(test_x, [test_c_y, test_s_y], verbose=0)\n",
    "loss, cla_loss, seg_loss, seg_loss2, cla_acc, seg_acc, seg_dice_coef = model.evaluate(test_x, [test_c_y, test_s_y, test_s_y], verbose=0)\n",
    "print('Test total loss:', loss)\n",
    "print('Test classification loss:', cla_loss)\n",
    "print('Test segmentation loss:', seg_loss)\n",
    "\n",
    "print('Test classification accuracy:', cla_acc)\n",
    "print('Test segmentation accuracy:', seg_acc)\n",
    "print('Test segmentation dice_coef:', seg_dice_coef)\n",
    "\n",
    "preds_c, preds_s, preds_s2 = model.predict(test_x, batch_size=8, verbose=1)\n",
    "test_mask = test_s_y.flatten()\n",
    "pred_mask = preds_s.flatten()\n",
    "fpr, tpr, thresholds = roc_curve(test_mask, pred_mask, pos_label=1)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC:\", roc_auc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3eb4e1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "Train on 1000 samples, validate on 64 samples\n",
      "Epoch 1/100\n",
      "1000/1000 [==============================] - 24s 24ms/step - loss: 0.0959 - classification_output_loss: 0.0651 - segmentation_output_loss: 0.1578 - seg-out_loss: 0.0603 - classification_output_acc: 0.9880 - segmentation_output_acc: 0.9410 - seg-out_dice_coef: 0.9397 - val_loss: 0.1967 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1429 - val_seg-out_loss: 0.0487 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9525 - val_seg-out_dice_coef: 0.9513\n",
      "Epoch 2/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0734 - classification_output_loss: 0.0708 - segmentation_output_loss: 0.1061 - seg-out_loss: 0.0430 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9588 - seg-out_dice_coef: 0.9570 - val_loss: 0.1853 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1162 - val_seg-out_loss: 0.0429 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9577 - val_seg-out_dice_coef: 0.9571\n",
      "Epoch 3/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0629 - classification_output_loss: 0.0748 - segmentation_output_loss: 0.0822 - seg-out_loss: 0.0335 - classification_output_acc: 0.9750 - segmentation_output_acc: 0.9683 - seg-out_dice_coef: 0.9665 - val_loss: 0.1924 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1314 - val_seg-out_loss: 0.0479 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9533 - val_seg-out_dice_coef: 0.9521\n",
      "Epoch 4/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0544 - classification_output_loss: 0.0724 - segmentation_output_loss: 0.0663 - seg-out_loss: 0.0270 - classification_output_acc: 0.9790 - segmentation_output_acc: 0.9748 - seg-out_dice_coef: 0.9730 - val_loss: 0.1936 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1362 - val_seg-out_loss: 0.0467 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9538 - val_seg-out_dice_coef: 0.9533\n",
      "Epoch 5/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0517 - classification_output_loss: 0.0732 - segmentation_output_loss: 0.0605 - seg-out_loss: 0.0245 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9773 - seg-out_dice_coef: 0.9755 - val_loss: 0.1964 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1447 - val_seg-out_loss: 0.0462 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9542 - val_seg-out_dice_coef: 0.9538\n",
      "Epoch 6/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0508 - classification_output_loss: 0.0739 - segmentation_output_loss: 0.0580 - seg-out_loss: 0.0238 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9780 - seg-out_dice_coef: 0.9762 - val_loss: 0.1964 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1473 - val_seg-out_loss: 0.0435 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9569 - val_seg-out_dice_coef: 0.9565\n",
      "Epoch 7/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0494 - classification_output_loss: 0.0793 - segmentation_output_loss: 0.0520 - seg-out_loss: 0.0213 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9804 - seg-out_dice_coef: 0.9787 - val_loss: 0.2001 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1542 - val_seg-out_loss: 0.0471 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9533 - val_seg-out_dice_coef: 0.9529\n",
      "Epoch 8/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0469 - classification_output_loss: 0.0766 - segmentation_output_loss: 0.0484 - seg-out_loss: 0.0200 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9816 - seg-out_dice_coef: 0.9800 - val_loss: 0.1969 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1488 - val_seg-out_loss: 0.0433 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9574 - val_seg-out_dice_coef: 0.9567\n",
      "Epoch 9/100\n",
      "1000/1000 [==============================] - 17s 17ms/step - loss: 0.0449 - classification_output_loss: 0.0729 - segmentation_output_loss: 0.0465 - seg-out_loss: 0.0192 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9824 - seg-out_dice_coef: 0.9808 - val_loss: 0.2091 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1743 - val_seg-out_loss: 0.0527 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9476 - val_seg-out_dice_coef: 0.9473\n",
      "Epoch 10/100\n",
      "1000/1000 [==============================] - 18s 18ms/step - loss: 0.0425 - classification_output_loss: 0.0679 - segmentation_output_loss: 0.0447 - seg-out_loss: 0.0185 - classification_output_acc: 0.9860 - segmentation_output_acc: 0.9831 - seg-out_dice_coef: 0.9815 - val_loss: 0.2002 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1582 - val_seg-out_loss: 0.0435 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9571 - val_seg-out_dice_coef: 0.9565\n",
      "Epoch 11/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0435 - classification_output_loss: 0.0728 - segmentation_output_loss: 0.0437 - seg-out_loss: 0.0183 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9832 - seg-out_dice_coef: 0.9817 - val_loss: 0.2005 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1596 - val_seg-out_loss: 0.0430 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9577 - val_seg-out_dice_coef: 0.9570\n",
      "Epoch 12/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0423 - classification_output_loss: 0.0724 - segmentation_output_loss: 0.0415 - seg-out_loss: 0.0174 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9842 - seg-out_dice_coef: 0.9826 - val_loss: 0.2039 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1648 - val_seg-out_loss: 0.0473 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9533 - val_seg-out_dice_coef: 0.9527\n",
      "Epoch 13/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0418 - classification_output_loss: 0.0722 - segmentation_output_loss: 0.0404 - seg-out_loss: 0.0171 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9844 - seg-out_dice_coef: 0.9829 - val_loss: 0.2059 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1709 - val_seg-out_loss: 0.0470 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9537 - val_seg-out_dice_coef: 0.9530\n",
      "Epoch 14/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0413 - classification_output_loss: 0.0726 - segmentation_output_loss: 0.0394 - seg-out_loss: 0.0165 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9850 - seg-out_dice_coef: 0.9835 - val_loss: 0.2032 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1660 - val_seg-out_loss: 0.0443 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9565 - val_seg-out_dice_coef: 0.9557\n",
      "Epoch 15/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0416 - classification_output_loss: 0.0743 - segmentation_output_loss: 0.0388 - seg-out_loss: 0.0163 - classification_output_acc: 0.9840 - segmentation_output_acc: 0.9852 - seg-out_dice_coef: 0.9837 - val_loss: 0.2071 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1731 - val_seg-out_loss: 0.0483 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9523 - val_seg-out_dice_coef: 0.9517\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0394 - classification_output_loss: 0.0695 - segmentation_output_loss: 0.0371 - seg-out_loss: 0.0158 - classification_output_acc: 0.9860 - segmentation_output_acc: 0.9856 - seg-out_dice_coef: 0.9842 - val_loss: 0.2042 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1676 - val_seg-out_loss: 0.0457 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9550 - val_seg-out_dice_coef: 0.9543\n",
      "Epoch 17/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0411 - classification_output_loss: 0.0782 - segmentation_output_loss: 0.0354 - seg-out_loss: 0.0151 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9862 - seg-out_dice_coef: 0.9849 - val_loss: 0.2026 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1651 - val_seg-out_loss: 0.0435 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9570 - val_seg-out_dice_coef: 0.9565\n",
      "Epoch 18/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0390 - classification_output_loss: 0.0699 - segmentation_output_loss: 0.0360 - seg-out_loss: 0.0154 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9860 - seg-out_dice_coef: 0.9846 - val_loss: 0.2045 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1685 - val_seg-out_loss: 0.0456 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9550 - val_seg-out_dice_coef: 0.9544\n",
      "Epoch 19/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0390 - classification_output_loss: 0.0711 - segmentation_output_loss: 0.0353 - seg-out_loss: 0.0152 - classification_output_acc: 0.9860 - segmentation_output_acc: 0.9863 - seg-out_dice_coef: 0.9848 - val_loss: 0.2031 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1672 - val_seg-out_loss: 0.0427 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9581 - val_seg-out_dice_coef: 0.9573\n",
      "Epoch 20/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0386 - classification_output_loss: 0.0709 - segmentation_output_loss: 0.0347 - seg-out_loss: 0.0150 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9864 - seg-out_dice_coef: 0.9850 - val_loss: 0.2037 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1670 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9562 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 21/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0383 - classification_output_loss: 0.0703 - segmentation_output_loss: 0.0343 - seg-out_loss: 0.0148 - classification_output_acc: 0.9840 - segmentation_output_acc: 0.9866 - seg-out_dice_coef: 0.9852 - val_loss: 0.2035 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1678 - val_seg-out_loss: 0.0433 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9574 - val_seg-out_dice_coef: 0.9567\n",
      "Epoch 22/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0376 - classification_output_loss: 0.0702 - segmentation_output_loss: 0.0330 - seg-out_loss: 0.0143 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9871 - seg-out_dice_coef: 0.9857 - val_loss: 0.2038 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1693 - val_seg-out_loss: 0.0426 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9580 - val_seg-out_dice_coef: 0.9574\n",
      "Epoch 23/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0388 - classification_output_loss: 0.0749 - segmentation_output_loss: 0.0325 - seg-out_loss: 0.0141 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9873 - seg-out_dice_coef: 0.9859 - val_loss: 0.2095 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1809 - val_seg-out_loss: 0.0474 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9532 - val_seg-out_dice_coef: 0.9526\n",
      "Epoch 24/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0389 - classification_output_loss: 0.0765 - segmentation_output_loss: 0.0317 - seg-out_loss: 0.0138 - classification_output_acc: 0.9740 - segmentation_output_acc: 0.9875 - seg-out_dice_coef: 0.9862 - val_loss: 0.2087 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1788 - val_seg-out_loss: 0.0471 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9535 - val_seg-out_dice_coef: 0.9529\n",
      "Epoch 25/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0367 - classification_output_loss: 0.0688 - segmentation_output_loss: 0.0319 - seg-out_loss: 0.0139 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9874 - seg-out_dice_coef: 0.9861 - val_loss: 0.2081 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1782 - val_seg-out_loss: 0.0461 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9546 - val_seg-out_dice_coef: 0.9539\n",
      "Epoch 26/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0382 - classification_output_loss: 0.0728 - segmentation_output_loss: 0.0327 - seg-out_loss: 0.0142 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9871 - seg-out_dice_coef: 0.9858 - val_loss: 0.2082 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1782 - val_seg-out_loss: 0.0463 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9544 - val_seg-out_dice_coef: 0.9537\n",
      "Epoch 27/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0379 - classification_output_loss: 0.0757 - segmentation_output_loss: 0.0302 - seg-out_loss: 0.0133 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9880 - seg-out_dice_coef: 0.9867 - val_loss: 0.2060 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1742 - val_seg-out_loss: 0.0442 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9564 - val_seg-out_dice_coef: 0.9558\n",
      "Epoch 28/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0388 - classification_output_loss: 0.0780 - segmentation_output_loss: 0.0307 - seg-out_loss: 0.0135 - classification_output_acc: 0.9790 - segmentation_output_acc: 0.9878 - seg-out_dice_coef: 0.9865 - val_loss: 0.2069 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1764 - val_seg-out_loss: 0.0444 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9563 - val_seg-out_dice_coef: 0.9556\n",
      "Epoch 29/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0398 - classification_output_loss: 0.0818 - segmentation_output_loss: 0.0303 - seg-out_loss: 0.0133 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9880 - seg-out_dice_coef: 0.9867 - val_loss: 0.2062 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1762 - val_seg-out_loss: 0.0427 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9580 - val_seg-out_dice_coef: 0.9573\n",
      "Epoch 30/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0359 - classification_output_loss: 0.0694 - segmentation_output_loss: 0.0300 - seg-out_loss: 0.0132 - classification_output_acc: 0.9870 - segmentation_output_acc: 0.9881 - seg-out_dice_coef: 0.9868 - val_loss: 0.2082 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1790 - val_seg-out_loss: 0.0455 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9552 - val_seg-out_dice_coef: 0.9545\n",
      "Epoch 31/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0384 - classification_output_loss: 0.0781 - segmentation_output_loss: 0.0295 - seg-out_loss: 0.0131 - classification_output_acc: 0.9730 - segmentation_output_acc: 0.9882 - seg-out_dice_coef: 0.9869 - val_loss: 0.2066 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1762 - val_seg-out_loss: 0.0437 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9569 - val_seg-out_dice_coef: 0.9563\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0385 - classification_output_loss: 0.0795 - segmentation_output_loss: 0.0291 - seg-out_loss: 0.0129 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9884 - seg-out_dice_coef: 0.9871 - val_loss: 0.2092 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1817 - val_seg-out_loss: 0.0457 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9550 - val_seg-out_dice_coef: 0.9543\n",
      "Epoch 33/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0360 - classification_output_loss: 0.0725 - segmentation_output_loss: 0.0282 - seg-out_loss: 0.0126 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9887 - seg-out_dice_coef: 0.9874 - val_loss: 0.2076 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1780 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 34/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0355 - classification_output_loss: 0.0710 - segmentation_output_loss: 0.0280 - seg-out_loss: 0.0125 - classification_output_acc: 0.9840 - segmentation_output_acc: 0.9888 - seg-out_dice_coef: 0.9875 - val_loss: 0.2103 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1839 - val_seg-out_loss: 0.0466 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9541 - val_seg-out_dice_coef: 0.9534\n",
      "Epoch 35/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0361 - classification_output_loss: 0.0729 - segmentation_output_loss: 0.0281 - seg-out_loss: 0.0125 - classification_output_acc: 0.9840 - segmentation_output_acc: 0.9887 - seg-out_dice_coef: 0.9875 - val_loss: 0.2081 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1799 - val_seg-out_loss: 0.0442 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9566 - val_seg-out_dice_coef: 0.9558\n",
      "Epoch 36/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0350 - classification_output_loss: 0.0691 - segmentation_output_loss: 0.0282 - seg-out_loss: 0.0126 - classification_output_acc: 0.9870 - segmentation_output_acc: 0.9887 - seg-out_dice_coef: 0.9874 - val_loss: 0.2072 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1781 - val_seg-out_loss: 0.0435 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9572 - val_seg-out_dice_coef: 0.9565\n",
      "Epoch 37/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0380 - classification_output_loss: 0.0785 - segmentation_output_loss: 0.0285 - seg-out_loss: 0.0128 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9885 - seg-out_dice_coef: 0.9872 - val_loss: 0.2101 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1840 - val_seg-out_loss: 0.0459 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9546 - val_seg-out_dice_coef: 0.9541\n",
      "Epoch 38/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0373 - classification_output_loss: 0.0775 - segmentation_output_loss: 0.0278 - seg-out_loss: 0.0125 - classification_output_acc: 0.9790 - segmentation_output_acc: 0.9888 - seg-out_dice_coef: 0.9875 - val_loss: 0.2081 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1801 - val_seg-out_loss: 0.0441 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9565 - val_seg-out_dice_coef: 0.9559\n",
      "Epoch 39/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0368 - classification_output_loss: 0.0773 - segmentation_output_loss: 0.0267 - seg-out_loss: 0.0121 - classification_output_acc: 0.9720 - segmentation_output_acc: 0.9891 - seg-out_dice_coef: 0.9879 - val_loss: 0.2072 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1784 - val_seg-out_loss: 0.0432 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9575 - val_seg-out_dice_coef: 0.9568\n",
      "Epoch 40/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0379 - classification_output_loss: 0.0801 - segmentation_output_loss: 0.0273 - seg-out_loss: 0.0123 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9889 - seg-out_dice_coef: 0.9877 - val_loss: 0.2110 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1857 - val_seg-out_loss: 0.0469 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9538 - val_seg-out_dice_coef: 0.9531\n",
      "Epoch 41/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0343 - classification_output_loss: 0.0693 - segmentation_output_loss: 0.0265 - seg-out_loss: 0.0120 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9891 - seg-out_dice_coef: 0.9880 - val_loss: 0.2091 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1831 - val_seg-out_loss: 0.0440 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9566 - val_seg-out_dice_coef: 0.9560\n",
      "Epoch 42/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0345 - classification_output_loss: 0.0697 - segmentation_output_loss: 0.0267 - seg-out_loss: 0.0121 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9892 - seg-out_dice_coef: 0.9879 - val_loss: 0.2108 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1857 - val_seg-out_loss: 0.0462 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9546 - val_seg-out_dice_coef: 0.9538\n",
      "Epoch 43/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0359 - classification_output_loss: 0.0753 - segmentation_output_loss: 0.0261 - seg-out_loss: 0.0118 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9894 - seg-out_dice_coef: 0.9882 - val_loss: 0.2095 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1838 - val_seg-out_loss: 0.0445 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9561 - val_seg-out_dice_coef: 0.9555\n",
      "Epoch 44/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0355 - classification_output_loss: 0.0739 - segmentation_output_loss: 0.0261 - seg-out_loss: 0.0119 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9893 - seg-out_dice_coef: 0.9881 - val_loss: 0.2098 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1846 - val_seg-out_loss: 0.0447 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9560 - val_seg-out_dice_coef: 0.9553\n",
      "Epoch 45/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0354 - classification_output_loss: 0.0736 - segmentation_output_loss: 0.0262 - seg-out_loss: 0.0118 - classification_output_acc: 0.9850 - segmentation_output_acc: 0.9894 - seg-out_dice_coef: 0.9882 - val_loss: 0.2093 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1838 - val_seg-out_loss: 0.0438 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9569 - val_seg-out_dice_coef: 0.9562\n",
      "Epoch 46/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0362 - classification_output_loss: 0.0761 - segmentation_output_loss: 0.0262 - seg-out_loss: 0.0119 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9894 - seg-out_dice_coef: 0.9881 - val_loss: 0.2108 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1871 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9558 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 47/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0364 - classification_output_loss: 0.0768 - segmentation_output_loss: 0.0263 - seg-out_loss: 0.0119 - classification_output_acc: 0.9740 - segmentation_output_acc: 0.9893 - seg-out_dice_coef: 0.9881 - val_loss: 0.2096 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1847 - val_seg-out_loss: 0.0440 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9567 - val_seg-out_dice_coef: 0.9560\n"
     ]
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     "text": [
      "Epoch 48/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0334 - classification_output_loss: 0.0685 - segmentation_output_loss: 0.0251 - seg-out_loss: 0.0115 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9897 - seg-out_dice_coef: 0.9885 - val_loss: 0.2091 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1836 - val_seg-out_loss: 0.0434 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9573 - val_seg-out_dice_coef: 0.9566\n",
      "Epoch 49/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0345 - classification_output_loss: 0.0718 - segmentation_output_loss: 0.0253 - seg-out_loss: 0.0116 - classification_output_acc: 0.9860 - segmentation_output_acc: 0.9896 - seg-out_dice_coef: 0.9884 - val_loss: 0.2097 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1845 - val_seg-out_loss: 0.0442 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9565 - val_seg-out_dice_coef: 0.9558\n",
      "Epoch 50/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0358 - classification_output_loss: 0.0780 - segmentation_output_loss: 0.0243 - seg-out_loss: 0.0112 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9900 - seg-out_dice_coef: 0.9888 - val_loss: 0.2117 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1893 - val_seg-out_loss: 0.0453 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9554 - val_seg-out_dice_coef: 0.9547\n",
      "Epoch 51/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0340 - classification_output_loss: 0.0711 - segmentation_output_loss: 0.0248 - seg-out_loss: 0.0114 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9898 - seg-out_dice_coef: 0.9886 - val_loss: 0.2103 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1859 - val_seg-out_loss: 0.0447 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9561 - val_seg-out_dice_coef: 0.9553\n",
      "Epoch 52/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0355 - classification_output_loss: 0.0766 - segmentation_output_loss: 0.0244 - seg-out_loss: 0.0112 - classification_output_acc: 0.9730 - segmentation_output_acc: 0.9900 - seg-out_dice_coef: 0.9888 - val_loss: 0.2102 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1858 - val_seg-out_loss: 0.0446 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9562 - val_seg-out_dice_coef: 0.9554\n",
      "Epoch 53/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0352 - classification_output_loss: 0.0757 - segmentation_output_loss: 0.0244 - seg-out_loss: 0.0113 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9899 - seg-out_dice_coef: 0.9887 - val_loss: 0.2125 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1913 - val_seg-out_loss: 0.0456 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9551 - val_seg-out_dice_coef: 0.9544\n",
      "Epoch 54/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0331 - classification_output_loss: 0.0693 - segmentation_output_loss: 0.0239 - seg-out_loss: 0.0111 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9900 - seg-out_dice_coef: 0.9889 - val_loss: 0.2112 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1886 - val_seg-out_loss: 0.0446 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9561 - val_seg-out_dice_coef: 0.9554\n",
      "Epoch 55/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0361 - classification_output_loss: 0.0795 - segmentation_output_loss: 0.0239 - seg-out_loss: 0.0111 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9901 - seg-out_dice_coef: 0.9889 - val_loss: 0.2108 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1881 - val_seg-out_loss: 0.0440 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9567 - val_seg-out_dice_coef: 0.9560\n",
      "Epoch 56/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0341 - classification_output_loss: 0.0729 - segmentation_output_loss: 0.0239 - seg-out_loss: 0.0110 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9901 - seg-out_dice_coef: 0.9890 - val_loss: 0.2136 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1938 - val_seg-out_loss: 0.0461 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9545 - val_seg-out_dice_coef: 0.9539\n",
      "Epoch 57/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0332 - classification_output_loss: 0.0695 - segmentation_output_loss: 0.0241 - seg-out_loss: 0.0111 - classification_output_acc: 0.9790 - segmentation_output_acc: 0.9900 - seg-out_dice_coef: 0.9889 - val_loss: 0.2107 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1874 - val_seg-out_loss: 0.0444 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9564 - val_seg-out_dice_coef: 0.9556\n",
      "Epoch 58/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0352 - classification_output_loss: 0.0773 - segmentation_output_loss: 0.0235 - seg-out_loss: 0.0109 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9902 - seg-out_dice_coef: 0.9891 - val_loss: 0.2110 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1886 - val_seg-out_loss: 0.0439 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9568 - val_seg-out_dice_coef: 0.9561\n",
      "Epoch 59/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0358 - classification_output_loss: 0.0789 - segmentation_output_loss: 0.0235 - seg-out_loss: 0.0110 - classification_output_acc: 0.9750 - segmentation_output_acc: 0.9902 - seg-out_dice_coef: 0.9890 - val_loss: 0.2108 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1874 - val_seg-out_loss: 0.0446 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9562 - val_seg-out_dice_coef: 0.9554\n",
      "Epoch 60/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0350 - classification_output_loss: 0.0742 - segmentation_output_loss: 0.0249 - seg-out_loss: 0.0115 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9897 - seg-out_dice_coef: 0.9885 - val_loss: 0.2168 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.2011 - val_seg-out_loss: 0.0482 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9526 - val_seg-out_dice_coef: 0.9518\n",
      "Epoch 61/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0347 - classification_output_loss: 0.0747 - segmentation_output_loss: 0.0239 - seg-out_loss: 0.0111 - classification_output_acc: 0.9840 - segmentation_output_acc: 0.9901 - seg-out_dice_coef: 0.9889 - val_loss: 0.2123 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1916 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 62/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0358 - classification_output_loss: 0.0774 - segmentation_output_loss: 0.0245 - seg-out_loss: 0.0114 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9898 - seg-out_dice_coef: 0.9886 - val_loss: 0.2127 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1925 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9558 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 63/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0320 - classification_output_loss: 0.0680 - segmentation_output_loss: 0.0226 - seg-out_loss: 0.0106 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9905 - seg-out_dice_coef: 0.9894 - val_loss: 0.2124 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1917 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9552\n"
     ]
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     "text": [
      "Epoch 64/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0338 - classification_output_loss: 0.0727 - segmentation_output_loss: 0.0233 - seg-out_loss: 0.0108 - classification_output_acc: 0.9850 - segmentation_output_acc: 0.9903 - seg-out_dice_coef: 0.9892 - val_loss: 0.2115 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1896 - val_seg-out_loss: 0.0443 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9565 - val_seg-out_dice_coef: 0.9557\n",
      "Epoch 65/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0367 - classification_output_loss: 0.0816 - segmentation_output_loss: 0.0239 - seg-out_loss: 0.0111 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9901 - seg-out_dice_coef: 0.9889 - val_loss: 0.2119 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1907 - val_seg-out_loss: 0.0443 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9565 - val_seg-out_dice_coef: 0.9557\n",
      "Epoch 66/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0341 - classification_output_loss: 0.0744 - segmentation_output_loss: 0.0228 - seg-out_loss: 0.0107 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9905 - seg-out_dice_coef: 0.9893 - val_loss: 0.2109 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1888 - val_seg-out_loss: 0.0434 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9573 - val_seg-out_dice_coef: 0.9566\n",
      "Epoch 67/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0328 - classification_output_loss: 0.0705 - segmentation_output_loss: 0.0227 - seg-out_loss: 0.0106 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9905 - seg-out_dice_coef: 0.9894 - val_loss: 0.2125 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1920 - val_seg-out_loss: 0.0449 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9551\n",
      "Epoch 68/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0331 - classification_output_loss: 0.0717 - segmentation_output_loss: 0.0226 - seg-out_loss: 0.0106 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9905 - seg-out_dice_coef: 0.9894 - val_loss: 0.2115 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1898 - val_seg-out_loss: 0.0441 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9566 - val_seg-out_dice_coef: 0.9559\n",
      "Epoch 69/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0334 - classification_output_loss: 0.0729 - segmentation_output_loss: 0.0224 - seg-out_loss: 0.0105 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9906 - seg-out_dice_coef: 0.9895 - val_loss: 0.2126 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1922 - val_seg-out_loss: 0.0449 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9558 - val_seg-out_dice_coef: 0.9551\n",
      "Epoch 70/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0334 - classification_output_loss: 0.0713 - segmentation_output_loss: 0.0234 - seg-out_loss: 0.0109 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9902 - seg-out_dice_coef: 0.9891 - val_loss: 0.2121 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1913 - val_seg-out_loss: 0.0443 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9564 - val_seg-out_dice_coef: 0.9557\n",
      "Epoch 71/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0328 - classification_output_loss: 0.0712 - segmentation_output_loss: 0.0222 - seg-out_loss: 0.0105 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9907 - seg-out_dice_coef: 0.9895 - val_loss: 0.2115 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1902 - val_seg-out_loss: 0.0438 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9569 - val_seg-out_dice_coef: 0.9562\n",
      "Epoch 72/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0340 - classification_output_loss: 0.0759 - segmentation_output_loss: 0.0218 - seg-out_loss: 0.0103 - classification_output_acc: 0.9850 - segmentation_output_acc: 0.9908 - seg-out_dice_coef: 0.9897 - val_loss: 0.2134 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1942 - val_seg-out_loss: 0.0453 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9555 - val_seg-out_dice_coef: 0.9547\n",
      "Epoch 73/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0342 - classification_output_loss: 0.0752 - segmentation_output_loss: 0.0226 - seg-out_loss: 0.0106 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9905 - seg-out_dice_coef: 0.9894 - val_loss: 0.2128 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1928 - val_seg-out_loss: 0.0449 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9551\n",
      "Epoch 74/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0337 - classification_output_loss: 0.0747 - segmentation_output_loss: 0.0220 - seg-out_loss: 0.0104 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9907 - seg-out_dice_coef: 0.9896 - val_loss: 0.2135 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1947 - val_seg-out_loss: 0.0449 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9558 - val_seg-out_dice_coef: 0.9551\n",
      "Epoch 75/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0337 - classification_output_loss: 0.0744 - segmentation_output_loss: 0.0221 - seg-out_loss: 0.0104 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9907 - seg-out_dice_coef: 0.9896 - val_loss: 0.2137 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1952 - val_seg-out_loss: 0.0451 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9556 - val_seg-out_dice_coef: 0.9549\n",
      "Epoch 76/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0349 - classification_output_loss: 0.0784 - segmentation_output_loss: 0.0220 - seg-out_loss: 0.0104 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9908 - seg-out_dice_coef: 0.9896 - val_loss: 0.2136 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1949 - val_seg-out_loss: 0.0450 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9557 - val_seg-out_dice_coef: 0.9550\n",
      "Epoch 77/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0322 - classification_output_loss: 0.0696 - segmentation_output_loss: 0.0220 - seg-out_loss: 0.0104 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9907 - seg-out_dice_coef: 0.9896 - val_loss: 0.2128 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1930 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 78/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0330 - classification_output_loss: 0.0723 - segmentation_output_loss: 0.0218 - seg-out_loss: 0.0104 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9907 - seg-out_dice_coef: 0.9896 - val_loss: 0.2132 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1941 - val_seg-out_loss: 0.0447 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9561 - val_seg-out_dice_coef: 0.9553\n",
      "Epoch 79/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0332 - classification_output_loss: 0.0737 - segmentation_output_loss: 0.0215 - seg-out_loss: 0.0102 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9909 - seg-out_dice_coef: 0.9898 - val_loss: 0.2138 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1957 - val_seg-out_loss: 0.0447 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9560 - val_seg-out_dice_coef: 0.9553\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0343 - classification_output_loss: 0.0769 - segmentation_output_loss: 0.0219 - seg-out_loss: 0.0103 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9908 - seg-out_dice_coef: 0.9897 - val_loss: 0.2142 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1968 - val_seg-out_loss: 0.0450 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9557 - val_seg-out_dice_coef: 0.9550\n",
      "Epoch 81/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0340 - classification_output_loss: 0.0762 - segmentation_output_loss: 0.0217 - seg-out_loss: 0.0103 - classification_output_acc: 0.9790 - segmentation_output_acc: 0.9908 - seg-out_dice_coef: 0.9897 - val_loss: 0.2139 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1957 - val_seg-out_loss: 0.0451 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9557 - val_seg-out_dice_coef: 0.9549\n",
      "Epoch 82/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0333 - classification_output_loss: 0.0745 - segmentation_output_loss: 0.0212 - seg-out_loss: 0.0101 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9911 - seg-out_dice_coef: 0.9899 - val_loss: 0.2156 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1994 - val_seg-out_loss: 0.0464 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9544 - val_seg-out_dice_coef: 0.9536\n",
      "Epoch 83/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0326 - classification_output_loss: 0.0723 - segmentation_output_loss: 0.0211 - seg-out_loss: 0.0101 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9910 - seg-out_dice_coef: 0.9899 - val_loss: 0.2134 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1949 - val_seg-out_loss: 0.0446 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9561 - val_seg-out_dice_coef: 0.9554\n",
      "Epoch 84/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0331 - classification_output_loss: 0.0735 - segmentation_output_loss: 0.0215 - seg-out_loss: 0.0102 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9909 - seg-out_dice_coef: 0.9898 - val_loss: 0.2160 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1998 - val_seg-out_loss: 0.0470 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9538 - val_seg-out_dice_coef: 0.9530\n",
      "Epoch 85/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0348 - classification_output_loss: 0.0802 - segmentation_output_loss: 0.0206 - seg-out_loss: 0.0099 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9912 - seg-out_dice_coef: 0.9901 - val_loss: 0.2134 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1951 - val_seg-out_loss: 0.0444 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9563 - val_seg-out_dice_coef: 0.9556\n",
      "Epoch 86/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0344 - classification_output_loss: 0.0787 - segmentation_output_loss: 0.0208 - seg-out_loss: 0.0100 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9911 - seg-out_dice_coef: 0.9900 - val_loss: 0.2141 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1964 - val_seg-out_loss: 0.0452 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9556 - val_seg-out_dice_coef: 0.9548\n",
      "Epoch 87/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0324 - classification_output_loss: 0.0712 - segmentation_output_loss: 0.0213 - seg-out_loss: 0.0102 - classification_output_acc: 0.9850 - segmentation_output_acc: 0.9909 - seg-out_dice_coef: 0.9898 - val_loss: 0.2147 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1977 - val_seg-out_loss: 0.0455 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9553 - val_seg-out_dice_coef: 0.9545\n",
      "Epoch 88/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0335 - classification_output_loss: 0.0750 - segmentation_output_loss: 0.0212 - seg-out_loss: 0.0102 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9910 - seg-out_dice_coef: 0.9898 - val_loss: 0.2140 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1963 - val_seg-out_loss: 0.0448 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9559 - val_seg-out_dice_coef: 0.9552\n",
      "Epoch 89/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0341 - classification_output_loss: 0.0771 - segmentation_output_loss: 0.0212 - seg-out_loss: 0.0101 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9910 - seg-out_dice_coef: 0.9899 - val_loss: 0.2149 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1984 - val_seg-out_loss: 0.0453 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9554 - val_seg-out_dice_coef: 0.9547\n",
      "Epoch 90/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0335 - classification_output_loss: 0.0757 - segmentation_output_loss: 0.0208 - seg-out_loss: 0.0100 - classification_output_acc: 0.9790 - segmentation_output_acc: 0.9911 - seg-out_dice_coef: 0.9900 - val_loss: 0.2151 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1989 - val_seg-out_loss: 0.0453 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9554 - val_seg-out_dice_coef: 0.9547\n",
      "Epoch 91/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0325 - classification_output_loss: 0.0733 - segmentation_output_loss: 0.0202 - seg-out_loss: 0.0098 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9902 - val_loss: 0.2146 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1982 - val_seg-out_loss: 0.0446 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9562 - val_seg-out_dice_coef: 0.9554\n",
      "Epoch 92/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0318 - classification_output_loss: 0.0705 - segmentation_output_loss: 0.0206 - seg-out_loss: 0.0099 - classification_output_acc: 0.9850 - segmentation_output_acc: 0.9912 - seg-out_dice_coef: 0.9901 - val_loss: 0.2148 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1979 - val_seg-out_loss: 0.0454 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9554 - val_seg-out_dice_coef: 0.9546\n",
      "Epoch 93/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0319 - classification_output_loss: 0.0715 - segmentation_output_loss: 0.0202 - seg-out_loss: 0.0097 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9903 - val_loss: 0.2144 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1973 - val_seg-out_loss: 0.0451 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9557 - val_seg-out_dice_coef: 0.9549\n",
      "Epoch 94/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0323 - classification_output_loss: 0.0727 - segmentation_output_loss: 0.0202 - seg-out_loss: 0.0097 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9903 - val_loss: 0.2140 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1969 - val_seg-out_loss: 0.0443 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9564 - val_seg-out_dice_coef: 0.9557\n",
      "Epoch 95/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0355 - classification_output_loss: 0.0819 - segmentation_output_loss: 0.0211 - seg-out_loss: 0.0101 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9910 - seg-out_dice_coef: 0.9899 - val_loss: 0.2137 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1960 - val_seg-out_loss: 0.0444 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9563 - val_seg-out_dice_coef: 0.9556\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0320 - classification_output_loss: 0.0717 - segmentation_output_loss: 0.0201 - seg-out_loss: 0.0098 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9902 - val_loss: 0.2141 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1968 - val_seg-out_loss: 0.0447 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9561 - val_seg-out_dice_coef: 0.9553\n",
      "Epoch 97/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0346 - classification_output_loss: 0.0802 - segmentation_output_loss: 0.0204 - seg-out_loss: 0.0098 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9902 - val_loss: 0.2154 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1996 - val_seg-out_loss: 0.0454 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9553 - val_seg-out_dice_coef: 0.9546\n",
      "Epoch 98/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0312 - classification_output_loss: 0.0687 - segmentation_output_loss: 0.0203 - seg-out_loss: 0.0098 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9902 - val_loss: 0.2155 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1996 - val_seg-out_loss: 0.0458 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9550 - val_seg-out_dice_coef: 0.9542\n",
      "Epoch 99/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0332 - classification_output_loss: 0.0756 - segmentation_output_loss: 0.0203 - seg-out_loss: 0.0098 - classification_output_acc: 0.9770 - segmentation_output_acc: 0.9913 - seg-out_dice_coef: 0.9902 - val_loss: 0.2146 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1981 - val_seg-out_loss: 0.0447 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9560 - val_seg-out_dice_coef: 0.9553\n",
      "Epoch 100/100\n",
      "1000/1000 [==============================] - 19s 19ms/step - loss: 0.0322 - classification_output_loss: 0.0730 - segmentation_output_loss: 0.0198 - seg-out_loss: 0.0096 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9914 - seg-out_dice_coef: 0.9904 - val_loss: 0.2151 - val_classification_output_loss: 0.4320 - val_segmentation_output_loss: 0.1993 - val_seg-out_loss: 0.0449 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9558 - val_seg-out_dice_coef: 0.9551\n",
      "================================================ Saving model ================================================\n",
      "model saved to: MTL_IBA_cross3_pretrain.h5\n"
     ]
    }
   ],
   "source": [
    "# print(\"================================================ begin training ================================================\")\n",
    "\n",
    "hist = model.fit(train_x,\n",
    "                 [train_c_y, train_s_y, train_s_y],\n",
    "                 batch_size=batch_size,\n",
    "                 epochs=100,\n",
    "                 validation_data=(test_x, [test_c_y, test_s_y, test_s_y]),\n",
    "                 verbose=1,\n",
    "                 callbacks=[reduce_lr, checkpoint_period1, csv_logger],\n",
    "                 shuffle=True)\n",
    "\n",
    "print(\"================================================ Saving model ================================================\")\n",
    "model_filename = Name + \".h5\"\n",
    "model.save_weights(model_filename)\n",
    "print('model saved to:', model_filename)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# print(\"================================================ begin training ================================================\")\n",
    "\n",
    "# hist = model.fit([train_x,train_x],\n",
    "#                  [train_c_y, train_s_y],\n",
    "#                  batch_size=batch_size,\n",
    "#                  epochs=200,\n",
    "#                  validation_data=([val_x,val_x], [val_c_y, val_s_y]),\n",
    "#                  verbose=1,\n",
    "#                  callbacks=[reduce_lr, csv_logger],\n",
    "#                  shuffle=True)\n",
    "\n",
    "# print(\"================================================ Saving model ================================================\")\n",
    "# model_filename = Name + \"4.h5\"\n",
    "# model.save_weights(model_filename)\n",
    "# print('model saved to:', model_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28078269",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (200,) and (100,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_27225/3741794800.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# plt.plot(N, hist.history[\"loss\"], label=\"train_loss\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"classification_output_loss\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train_c_loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"segmentation_output_loss\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train_s_loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2757\u001b[0m     return gca().plot(\n\u001b[1;32m   2758\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2759\u001b[0;31m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1630\u001b[0m         \"\"\"\n\u001b[1;32m   1631\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1632\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1633\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1634\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    499\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    500\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (200,) and (100,)"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = np.arange(0, EPOCHS)\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "# plt.plot(N, hist.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(N, hist.history[\"classification_output_loss\"], label=\"train_c_loss\")\n",
    "plt.plot(N, hist.history[\"segmentation_output_loss\"], label=\"train_s_loss\")\n",
    "\n",
    "plt.plot(N, hist.history[\"classification_output_acc\"], label=\"train_c_acc\")\n",
    "plt.plot(N, hist.history[\"segmentation_output_acc\"], label=\"train_s_acc\")\n",
    "plt.plot(N, hist.history[\"seg-out_dice_coef\"], label=\"train_s_dice\")\n",
    "\n",
    "# plt.plot(N, hist.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(N, hist.history[\"val_classification_output_loss\"], label=\"val_c_loss\")\n",
    "plt.plot(N, hist.history[\"val_segmentation_output_loss\"], label=\"val_s_loss\")\n",
    "\n",
    "plt.plot(N, hist.history[\"val_classification_output_acc\"], label=\"val_c_acc\")\n",
    "plt.plot(N, hist.history[\"val_segmentation_output_acc\"], label=\"val_s_acc\")\n",
    "plt.plot(N, hist.history[\"val_seg-out_dice_coef\"], label=\"val_s_dice\")\n",
    "\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy/DSC\")\n",
    "plt.legend()\n",
    "plt.savefig(os.path.join('output/', Name+'.png'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "452674d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------Start predicting------\n",
      "------------------------------------------------ Classification testing ------------------------------------------------\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "   bengin_images   0.895833  1.000000  0.945055        43\n",
      "malignant_images   1.000000  0.761905  0.864865        21\n",
      "\n",
      "        accuracy                       0.921875        64\n",
      "       macro avg   0.947917  0.880952  0.904960        64\n",
      "    weighted avg   0.930013  0.921875  0.918743        64\n",
      "\n",
      "------------------------------------------------ Segmentation testing ------------------------------------------------\n",
      "Test total loss: 0.21510875597596169\n",
      "Test classification loss: 0.43203072249889374\n",
      "Test segmentation loss: 0.19934973865747452\n",
      "Test classification accuracy: 0.921875\n",
      "Test segmentation accuracy: 0.9558002650737762\n",
      "Test segmentation dice_coef: 0.9550652801990509\n",
      "64/64 [==============================] - 0s 8ms/step\n",
      "AUC: 0.9860011957940111\n"
     ]
    }
   ],
   "source": [
    "print(\"------Start predicting------\")\n",
    "\n",
    "\n",
    "\n",
    "predictions_c, predictions_s, predictions_s2 = model.predict(test_x, batch_size=32)\n",
    "\n",
    "\n",
    "print(\"------------------------------------------------ Classification testing ------------------------------------------------\")\n",
    "\n",
    "print(classification_report(test_c_y.argmax(axis=1), predictions_c.argmax(axis=1), target_names=lb.classes_, digits=6))\n",
    "\n",
    "print(\"------------------------------------------------ Segmentation testing ------------------------------------------------\")\n",
    "\n",
    "\n",
    "# evaluate the model\n",
    "# loss = model.evaluate(test_x, [test_c_y, test_s_y], verbose=0)\n",
    "loss, cla_loss, seg_loss, seg_loss2, cla_acc, seg_acc, seg_dice_coef = model.evaluate(test_x, [test_c_y, test_s_y, test_s_y], verbose=0)\n",
    "print('Test total loss:', loss)\n",
    "print('Test classification loss:', cla_loss)\n",
    "print('Test segmentation loss:', seg_loss)\n",
    "\n",
    "print('Test classification accuracy:', cla_acc)\n",
    "print('Test segmentation accuracy:', seg_acc)\n",
    "print('Test segmentation dice_coef:', seg_dice_coef)\n",
    "\n",
    "preds_c, preds_s, preds_s2 = model.predict(test_x, batch_size=8, verbose=1)\n",
    "test_mask = test_s_y.flatten()\n",
    "pred_mask = preds_s.flatten()\n",
    "fpr, tpr, thresholds = roc_curve(test_mask, pred_mask, pos_label=1)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC:\", roc_auc)\n",
    "\n",
    "# Precision, Recall, accuracy, F1, IoU = p_r_f1_iou(test_s_y, preds_s)\n",
    "# print('Precision:', Precision)\n",
    "# print('Recall:', Recall)\n",
    "# print('m_Iou:', IoU)\n",
    "# print('F1_score:', F1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ec52ea",
   "metadata": {},
   "source": [
    "# 生成肿块分割预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2959c28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask2gray(mask, input_type=None):\n",
    "    if input_type is 'pred':\n",
    "        mask0 = mask[:, :, 0]\n",
    "        mask1 = mask[:, :, 1]\n",
    "        mask = mask1 - mask0\n",
    "        print(\"mask.shape: \", mask.shape)\n",
    "        #mask = np.argmax(mask, axis=-1)\n",
    "        # print( mask[mask > 0.5] )\n",
    "        mask[mask > 0] = 255\n",
    "        mask[mask <= 0] = 0\n",
    "    mask = mask.astype(dtype=np.uint8)\n",
    "    rst = mask.copy()\n",
    "    cv.normalize(mask, rst, alpha=0, beta=255, norm_type=cv.NORM_MINMAX)\n",
    "    return rst\n",
    "\n",
    "print(\"------------------------------ Saving predictions ------------------------------------\")\n",
    "\n",
    "import glob\n",
    "img_size = target\n",
    "preds_savePath = os.path.join(\"./seg_prediction/BUSI/\", Name)\n",
    "class_path_list = glob.glob(testX_dir+'*\\\\*')\n",
    "test_img_Names = []\n",
    "for class_path in class_path_list:\n",
    "    all_images_list = glob.glob(class_path + '*\\\\*')\n",
    "    print(all_images_list)\n",
    "    for image_path in all_images_list:\n",
    "        print(image_path)\n",
    "#         images_name = image_path.split()\n",
    "\n",
    "# for i in range(test_x.shape[0]):\n",
    "#     img = test_orignal_images[i]\n",
    "\n",
    "#     gt = test_Y[i, :, :, 1]\n",
    "#     gt = mask2gray(gt)\n",
    "\n",
    "#     pred = preds[i]\n",
    "#     prediction = mask2gray(pred, input_type='pred')\n",
    "\n",
    "#     save_img = np.zeros([img_size[0], img_size[1]*3, 3], dtype=np.uint8)\n",
    "#     save_img[:, 0:img_size[0], :] = img[:, :, ::-1]\n",
    "#     save_img[:, img_size[0]:img_size[1] * 2, :] = cv.cvtColor(gt, cv.COLOR_GRAY2RGB)\n",
    "#     save_img[:, img_size[0] * 2:img_size[1] * 3, :] = cv.cvtColor(prediction, cv.COLOR_GRAY2RGB)\n",
    "\n",
    "#     savePath = os.path.join(preds_savePath, \"joint\")\n",
    "\n",
    "#     if not os.path.exists(savePath):\n",
    "#         os.makedirs(savePath)\n",
    "\n",
    "#     cv.imwrite(savePath + \"{0}\".format(test_img_Names[i]), save_img)\n",
    "\n",
    "# for i in range(test_x.shape[0]):\n",
    "\n",
    "#     img = test_orignal_images[i]\n",
    "\n",
    "#     gt = test_Y[i, :, :, 1]\n",
    "#     gt = mask2gray(gt)\n",
    "#     gt = cv.cvtColor(gt, cv.COLOR_GRAY2RGB)\n",
    "\n",
    "#     pred_mask = preds[i]\n",
    "#     pred_mask = mask2gray(pred_mask, input_type='pred_mask')\n",
    "\n",
    "#     savePath = os.path.join(preds_savePath, \"Imgs/\")\n",
    "#     saveMaskPath = os.path.join(preds_savePath, \"Mask/\")\n",
    "#     savePredPath = os.path.join(preds_savePath, \"Pred/\")\n",
    "\n",
    "#     if not os.path.exists(savePath):\n",
    "#         os.makedirs(savePath)\n",
    "#     if not os.path.exists(saveMaskPath):\n",
    "#         os.makedirs(saveMaskPath)\n",
    "#     if not os.path.exists(savePredPath):\n",
    "#         os.makedirs(savePredPath)\n",
    "\n",
    "#     cv.imwrite(os.path.join(savePath, \"{0}\".format(test_img_Names[i])), img)\n",
    "#     cv.imwrite(os.path.join(saveMaskPath, \"{0}\".format(test_img_Names[i])), gt)\n",
    "#     cv.imwrite(os.path.join(savePredPath, \"{0}\".format(test_img_Names[i])), pred)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "695c4f86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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